KEYWORDS: Unmanned aerial vehicles, Land mines, Sensors, Situational awareness sensors, Detection and tracking algorithms, Target detection, Systems modeling, Stochastic processes, Control systems
Multi-agent hybrid dynamical systems are a natural model for collaborative missions in which several steps and behaviors are required to achieve the goal of the mission. Missions are tasks featuring interacting subtasks, such as the decision of where to search, how to search, and when to transition from a search behavior to a rescue behavior. While the discrete nature of mission actions (which subtask to accomplish) and the continuous nature of real-world physical state spaces make hybrid systems a good model, control in such systems is poorly understood. Theoretical results on state reachability rely on restrictive assumptions which hinder formal verification and optimization of such systems. Despite this, we find the formalism to have significant value and develop hierarchical state estimation tools to control agents in a hybrid framework and execute missions. In past work, we developed hierarchical dynamic target modeling to estimate the progress of search and track scenarios with moving targets. In this work, we consider the related problem of searching for stationary targets that appear in formation. While this may seem easier than searching for moving targets (e.g. because a preplanned search is guaranteed to find all targets), executing the search efficiently and gaining situational awareness while doing so presents unique challenges. We develop a generative hierarchical model for target locations that relies on stochastic clustering techniques and ideas from object Simultaneous Location and Mapping (SLAM) to address these challenges and demonstrate their efficacy in single- and multi-agent scenarios.
We explore the use of transfer learning to reduce the data and computing resources required for training convolutional neural networks used by autonomous vehicles for predicting target behavior and improving target tracking as the scenario/environment changes. We demonstrate the ability to adapt to four different changes to the baseline scenario: a new target behavior, mission, adversary, and environment. The results from all four scenarios demonstrate positive transfer learning with reduced training datasets and show that transfer learning is a robust approach to dealing with changing environments even when the input or output dimensions of the neural network are changed.
Multi agent hybrid dynamical systems are a natural model for collaborative missions in which several steps and behaviors are required to achieve the goal of the mission. Missions are tasks featuring interacting subtasks, such as the decision of where to search, how to search, and when to transition from a search behavior to a rescue behavior. Control in hybrid systems is poorly understood. Theoretical results on state reachability rely on restrictive assumptions which hinder formal verification and optimization of such systems. Further difficulties arise if there are no a priori ordering or termination conditions on the intermediate steps and behaviors. We present a flexible framework to enable decentralized multi agent hybrid control and demonstrate its efficacy in a class of multi-region search and rescue scenarios. We also demonstrate the importance of dynamic target modeling at both levels of the hybrid state, i.e. estimating which region targets are in, how search behavior affects this estimate, and how the targets move between and within regions.
As warfare looks to the future and the need for the internet of military things (IoMT) grows, we discuss how autonomy fits into this paradigm. We define common terms relating to autonomy to promote common understanding between autonomy developers, and we analyze a variety of autonomy architectures, examining what they do correctly to support IoMT and where they fall short. We discuss our general philosophy concerning autonomy – that it must be multi-layered to be effective – and provide an overview for our Modular, Extensible, Interoperable Autonomy architecture that supports IoMT and the future of warfare.
Control in multi-agent hybrid dynamical systems – systems in which the state contains both discrete and continuous elements – is poorly understood. Theoretical results on state reachability and avoidablility typically rely on restrictive assumptions which do not hold in many important cases, hampering results in both trust and optimization of such systems. We introduce a flexible framework to enable control in multi-agent hybrid dynamical systems. We present an agent-based finite horizon temporal logic (FHTL) framework that enables mission monitoring and improves agent to agent collaboration in multi-agent hybrid systems under significantly lighter assumptions than required for similar infinite horizon temporal logic (IHTL) applications. We demonstrate our framework in an example scenario and provide both quantitative and qualitative analyses of the performance gains and mission trust monitoring enabled by our tools for this example.
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